David Camarillo

Assistant Professor of Bioengineering and, by courtesy, of Mechanical Engineering

Bio

Bio

David Camarillo is an Assistant Professor of Bioengineering and Mechanical Engineering (by courtesy) at Stanford University. Dr. Camarillo holds a B.S.E in Mechanical and Aerospace Engineering from Princeton University, and a Ph.D. in Mechanical Engineering from Stanford. Both his graduate work and industry experience with Intuitive Surgical and Hansen Medical were in medical device design, specifically the area of surgical robotics. Dr. Camarillo performed his postdoctoral research in Biophysics at the University of California, San Francisco in 2011. He is an expert in instrumentation and biomechanics whose research interests include medical technology design over a broad range of applications from prevention of mild traumatic brain injury, prediction of embryo viability, and cardiovascular robotic surgery. He directs a National Institute of Health (NIH) funded laboratory working to solve these problems.

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Research & Scholarship

Current Research and Scholarly Interests

The Camarillo Lab is currently instrumenting Stanford athletes with inertial sensors to investigate the mechanism of concussion. We are also characterizing the response of head-blows through imaging, blood, and other neurophysiological measurements. Understanding the mechanism of concussion will allow for change of rules, technique, or the development of preventive equipment and diagnostics to reduce brain injuries. Additionally, the lab is researching cell mechanics for regenerative medicine. We are developing a quantitative, noninvasive and early (day 1) measure of viability in order to allow clinicians to transfer the single most viable embryo, reducing the incidence of multiple gestations while preserving the pregnancy and birth rate of IVF. Another area of research is in medical instrumentation as it pertains to robotic catheterization for curing cardiac arrhythmia. We aim to improve the usability and improve the safety of the process of cardiac catheter ablation through robotic control. We are currently investigating new control methods, medical image guidance, and automation for robotic catheter procedures

Abstract

Injury from blunt head impacts causes acute neurological deficits and may lead to chronic neurodegeneration. A head impact detection device can serve both as a research tool for studying head injury mechanisms and a clinical tool for real-time trauma screening. The simplest approach is an acceleration thresholding algorithm, which may falsely detect high-acceleration spurious events such as manual manipulation of the device. We designed a head impact detection system that distinguishes head impacts from non-impacts through two subsystems. First, we use infrared proximity sensing to determine if the mouthguard is worn on the teeth to filter out all offteeth events. Second, on-teeth, non-impact events are rejected using a support vector machine classifier trained on frequency domain features of linear acceleration and rotational velocity. The remaining events are classified as head impacts. In a controlled laboratory evaluation, the present system performed substantially better than a 10g acceleration threshold in head impact detection (98% sensitivity, 99.99% specificity, 99% accuracy, and 99.98% precision, compared to 92% sensitivity, 58% specificity, 65% accuracy, and 37% precision). Once adapted for field deployment by training and validation with field data, this system has the potential to effectively detect head trauma in sports, military service, and other high-risk activities.

Abstract

Cell-matrix and cell-cell mechanosensing are important in many cellular processes, particularly for epithelial cells. A crucial question, which remains unexplored, is how the mechanical microenvironment is altered as a result of changes to multicellular tissue structure during cancer progression. In this study, we investigated the influence of the multicellular tissue architecture on mechanical properties of the epithelial component of the mammary acinus. Using creep compression tests on multicellular breast epithelial structures, we found that pre-malignant acini with no lumen (MCF10AT) were significantly stiffer than normal hollow acini (MCF10A) by 60%. This difference depended on structural changes in the pre-malignant acini, as neither single cells nor normal multicellular acini tested before lumen formation exhibited these differences. To understand these differences, we simulated the deformation of the acini with different multicellular architectures and calculated their mechanical properties; our results suggest that lumen filling alone can explain the experimentally observed stiffness increase. We also simulated a single contracting cell in different multicellular architectures and found that lumen filling led to a 20% increase in the "perceived stiffness" of a single contracting cell independent of any changes to matrix mechanics. Our results suggest that lumen filling in carcinogenesis alters the mechanical microenvironment in multicellular epithelial structures, a phenotype that may cause downstream disruptions to mechanosensing.

Abstract

The Stanford Biodesign Program began in 2001 with a mission of helping to train leaders in biomedical technology innovation. A key feature of the program is a full-time postgraduate fellowship where multidisciplinary teams undergo a process of sourcing clinical needs, inventing solutions and planning for implementation of a business strategy. The program places a priority on needs identification, a formal process of selecting, researching and characterizing needs before beginning the process of inventing. Fellows and students from the program have gone on to careers that emphasize technology innovation across industry and academia. Biodesign trainees have started 26 companies within the program that have raised over $200 million and led to the creation of over 500 new jobs. More importantly, although most of these technologies are still at a very early stage, several projects have received regulatory approval and so far more than 150,000 patients have been treated by technologies invented by our trainees. This paper reviews the initial outcomes of the program and discusses lessons learned and future directions in terms of training priorities.

Abstract

The purpose of this study was to evaluate a novel instrumented mouthguard as a research device for measuring head impact kinematics. To evaluate kinematic accuracy, laboratory impact testing was performed at sites on the helmet and facemask for determining how closely instrumented mouthguard data matched data from an anthropomorphic test device. Laboratory testing results showed that peak linear acceleration (r (2) = 0.96), peak angular acceleration (r (2) = 0.89), and peak angular velocity (r (2) = 0.98) measurements were highly correlated between the instrumented mouthguard and anthropomorphic test device. Normalized root-mean-square errors for impact time traces were 9.9 ± 4.4% for linear acceleration, 9.7 ± 7.0% for angular acceleration, and 10.4 ± 9.9% for angular velocity. This study demonstrates the potential of an instrumented mouthguard as a research tool for measuring in vivo impacts, which could help uncover the link between head impact kinematics and brain injury in American football.

Abstract

Recent advances in optical imaging have led to the development of miniature microscopes that can be brought to the patient for visualizing tissue structures in vivo. These devices have the potential to revolutionize health care by replacing tissue biopsy with in vivo pathology. One of the primary limitations of these microscopes, however, is that the constrained field of view can make image interpretation and navigation difficult. In this paper, we show that image mosaicing can be a powerful tool for widening the field of view and creating image maps of microanatomical structures. First, we present an efficient algorithm for pairwise image mosaicing that can be implemented in real time. Then, we address two of the main challenges associated with image mosaicing in medical applications: cumulative image registration errors and scene deformation. To deal with cumulative errors, we present a global alignment algorithm that draws upon techniques commonly used in probabilistic robotics. To accommodate scene deformation, we present a local alignment algorithm that incorporates deformable surface models into the mosaicing framework. These algorithms are demonstrated on image sequences acquired in vivo with various imaging devices including a hand-held dual-axes confocal microscope, a miniature two-photon microscope, and a commercially available confocal microendoscope.

Abstract

In this paper we describe the development of a robotically-assisted image mosaicing system for medical applications. The processing occurs in real-time due to a fast initial image alignment provided by robotic position sensing. Near-field imaging, defined by relatively large camera motion, requires translations as well as pan and tilt orientations to be measured. To capture these measurements we use 5-d.o.f. sensing along with a hand-eye calibration to account for sensor offset. This sensor-based approach speeds up the mosaicing, eliminates cumulative errors, and readily handles arbitrary camera motions. Our results have produced visually satisfactory mosaics on a dental model but can be extended to other medical images.

Abstract

It has been nearly 20 years since the first appearance of robotics in the operating room. In that time, much progress has been made in integrating robotic technologies with surgical instrumentation, as evidenced by the many thousands of successful robot-assisted cases. However, to build on past success and to fully leverage the potential of surgical robotics in the future, it is essential to maximize a shared understanding and communication among surgeons, engineers, entrepreneurs, and healthcare administrators. This article provides an introduction to medical robotic technologies, develops a possible taxonomy, reviews the evolution of a surgical robot, and discusses future prospects for innovation. Robotic surgery has demonstrated some clear benefits. It remains to be seen where these benefits will outweigh the associated costs over the long term. In the future, surgical robots should be smaller, less expensive, easier to operate, and should seamlessly integrate emerging technologies from a number of different fields. Such advances will enable continued progress in surgical instrumentation and, ultimately, surgical care.